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Proceedings Paper

Arteriovenous classification method using convolutional neural network for early detection of retinal vascular lesion
Author(s): Hibiki Ikawa; Yuji Hatanaka; Wataru Sunayama; Kazunori Ogohara; Chisako Muramatsu; Hiroshi Fujita
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Paper Abstract

Early detection of hypertension is important because hypertension leads to stroke and cardiovascular diseases. Hypertensive changes in the retina are diagnosed by measuring the arteriovenous ratio near the optic disc. Therefore, classification of arteries and veins is necessary for ratio measurement, and previous studies classified them by using pixel-based features, such as pixel values, texture features, and shape features etc. For simplification of the classification process, a convolutional neural network (CNN) was applied in this study. For evaluation of the classification process, CNN was tested using centerlines extracted manually in this study. As a result of a fourfold cross-validation with 40 retinal images, the mean classification ratio of the arteries and veins was 98%. Furthermore, CNN was tested using the centerlines of blood vessels automatically extracted using the CNN-based method for testing the fully automatic method. CNN classified 90% of blood vessels into arteries and veins in the arteriovenous ratio measurement zone. CNN had 30 trained and 10 tested retinal images. This result may work as an important processing for abnormality detection.

Paper Details

Date Published: 27 March 2019
PDF: 5 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110501M (27 March 2019); doi: 10.1117/12.2521528
Show Author Affiliations
Hibiki Ikawa, Univ. of Shiga Prefecture (Japan)
Yuji Hatanaka, Univ. of Shiga Prefecture (Japan)
Wataru Sunayama, Univ. of Shiga Prefecture (Japan)
Kazunori Ogohara, Univ. of Shiga Prefecture (Japan)
Chisako Muramatsu, Gifu Univ. (Japan)
Hiroshi Fujita, Gifu Univ. (Japan)

Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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